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213 lines
10 KiB
Python
213 lines
10 KiB
Python
"""Writing your own indicators — the ones the library does not ship.
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Demonstrates:
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- an indicator as a plain function returning an `Expr`
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- `scan` for stateful indicators no rolling window can express
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- `param(...)` to make a custom indicator sweepable
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Data: shared store — real market data from `data/` (see examples/README.md)
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Usage:
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python examples/19_custom_indicators.py
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────────────────────────────────────────────────────────────────────────────
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THE MENTAL MODEL
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────────────────────────────────────────────────────────────────────────────
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An indicator here is NOTHING but a Python function returning an `Expr`. An
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`Expr` is a *node in a computation graph*: writing `(high + low) / 2` touches
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no data — it describes an operation. The whole graph is then compiled and
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evaluated **in Rust**, in one vectorised pass. That is why your own indicators
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run at the speed of the built-in ones: they end up in the same engine.
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The whole `manifoldbt.indicators` library is written this way (`sma` ==
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`source.rolling_mean(period)`). So "adding an indicator" means "writing a
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function that composes `Expr`s". Three levels, from the common to the rare.
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"""
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import os
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from time import perf_counter
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import manifoldbt as mbt
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# Base columns (already Exprs) plus a few helpers.
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from manifoldbt.indicators import open, high, low, close, volume, sma, rsi, ema
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# Low-level bricks: lit (constant), col (column by name), when (if/else),
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# scan/s (recursive state), param (sweepable parameter).
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from manifoldbt.expr import lit, col, when, scan, s, param
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from manifoldbt.helpers import time_range, Slippage, Interval
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# ═══════════════════════════════════════════════════════════════════════════
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# LEVEL 1 — COMPOSING PRIMITIVES (99% of cases)
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# ═══════════════════════════════════════════════════════════════════════════
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# Combine columns + operators (+ - * /, > < >= & | ~) + Expr methods
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# (rolling_mean/std/min/max/median, ewm_mean, zscore, pct_change, diff, lag,
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# rsi, linreg_*, cross_above/below, cumsum, rank, ...). Every call returns
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# an Expr, so everything chains.
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def awesome_oscillator(fast=5, slow=34):
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"""Awesome Oscillator (Bill Williams) — NOT in the library.
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AO = SMA(median price, 5) − SMA(median price, 34), median = (H+L)/2
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Momentum: positive means buying pressure, negative means selling.
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"""
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median_price = (high + low) / 2 # Expr: an operation on 2 columns
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return sma(median_price, fast) - sma(median_price, slow) # the result Expr
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def dist_to_ma_pct(period=20):
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"""Distance from price to its moving average, in % — NOT in the library.
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Negative means the price sits BELOW its average (oversold), which makes it
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a natural building block for mean reversion. One line of composition.
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"""
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ma = sma(close, period)
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return (close - ma) / ma * 100.0
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def intraday_range_pct():
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"""Bar range as a % of the close — NOT in the library.
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An instant volatility proxy. Shows that OHLC columns mix freely.
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"""
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return (high - low) / close * 100.0
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def rsi_zscore(period=14, lookback=365):
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"""Standardised RSI: how extreme the RSI is against ITS OWN history.
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Composes a built-in indicator (rsi) with rolling statistics — the same
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pattern used in strategies/rsi_dynamic_alloc.py.
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"""
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r = rsi(close, period)
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return (r - r.rolling_mean(lookback)) / r.rolling_std(lookback)
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# ═══════════════════════════════════════════════════════════════════════════
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# LEVEL 2 — `scan`: STATEFUL / RECURSIVE INDICATORS
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# ═══════════════════════════════════════════════════════════════════════════
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# When today's value depends on YESTERDAY's (recursion) and no rolling window
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# suffices, reach for `scan`. It runs as a small scalar VM, entirely in Rust
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# (no Python callback per bar).
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#
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# scan(state=..., update=..., output=...)
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# • state : state variables and their initial value (first row)
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# • update : expressions evaluated on every bar, IN ORDER
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# - s.prev("x") = value of "x" on the previous bar
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# - s.var("k") = value computed earlier WITHIN THE SAME step
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# - an update name matching a state name rewrites that state
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# • output : which variable to emit as the result
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#
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# Proof that it is enough: the shipped Kalman and GARCH are written with scan
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# ALONE (see manifoldbt/indicators.py).
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def up_streak():
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"""Count of consecutive UP bars — NOT in the library, and impossible with
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a plain rolling window (it needs a counter that resets).
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streak = previous streak + 1 if close > close(-1), else 0
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"""
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is_up = close > close.lag(1) # boolean Expr (1.0 / 0.0) per bar
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return scan(
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state={"n": lit(0.0)}, # counter seeded at 0
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update={
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# if is_up: prev(n) + 1 else: 0
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"n": when(is_up, s.prev("n") + lit(1.0), lit(0.0)),
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},
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output="n",
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)
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def ema_from_scratch(alpha=0.1):
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"""A hand-rolled EMA via scan — purely to show the mechanism.
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(EMA is built in: `ema(close, span)`. This one is pedagogical.)
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ema = alpha * close + (1 - alpha) * previous ema
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"""
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return scan(
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state={"ema": close}, # seeded with the first close
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update={"ema": lit(alpha) * close + lit(1.0 - alpha) * s.prev("ema")},
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output="ema",
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)
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# ═══════════════════════════════════════════════════════════════════════════
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# LEVEL 3 — THE LIMITS (WORTH KNOWING)
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# ═══════════════════════════════════════════════════════════════════════════
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# • NO Python callback per bar: `scan` runs in Rust, and you cannot inject a
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# Python function called on every candle (it would be slow). As long as the
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# logic expresses in Expr + when + scan, it works.
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# • A GENUINELY new indicator, not expressible that way, needs a new `Expr`
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# variant and its Rust kernel — the contributor path, not the user path.
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# • External data (hashrate, funding, sentiment…): `mbt.register_exo(...)`,
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# then `exo("name")` returns an Expr usable like any other column.
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# ═══════════════════════════════════════════════════════════════════════════
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# BONUS — MAKING YOUR INDICATOR SWEEPABLE
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# ═══════════════════════════════════════════════════════════════════════════
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# Periods accept `param(...)` in place of an integer. The engine then
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# recompiles once per combination and sweeps the grid in parallel, without
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# changing a line of the indicator:
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#
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# ao = awesome_oscillator(fast=param("fast"), slow=param("slow"))
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# # then, with the grid passed separately (the indicator is unchanged):
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# # batch = mbt.run_sweep_lite(
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# # strategy,
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# # {"fast": [3, 5, 8], "slow": [21, 34, 55]},
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# # config, store,
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# # )
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#
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# (see examples/08_sweep_2d_heatmap.py for the full sweep.)
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# ═══════════════════════════════════════════════════════════════════════════
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# PUTTING A CUSTOM INDICATOR IN A STRATEGY AND BACKTESTING IT
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# ═══════════════════════════════════════════════════════════════════════════
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# Using `dist_to_ma_pct` (mean reversion): long when the price sits well below
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# its average, out when it has caught up.
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dist = dist_to_ma_pct(period=48) # our custom indicator
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streak = up_streak() # a second one, exposed too
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signal = when(dist < -5.0, 1.0, # >5% below the MA -> buy the dip
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when(dist > 0.0, 0.0)) # back at the MA -> exit, else hold
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strategy = (
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mbt.Strategy.create("custom_indicator_demo")
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.signal("dist_to_ma_%", dist) # .signal() exposes it in the report
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.signal("up_streak", streak)
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.size(signal)
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.describe("Mean reversion driven by a custom indicator (distance to the MA)")
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)
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# -- Config -------------------------------------------------------------------
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start, end = time_range("2021-01-01", "2026-01-01")
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config = mbt.BacktestConfig(
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universe={"binance": ["BTC-USDT:perp"]},
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time_range_start=start,
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time_range_end=end,
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bar_interval=Interval.hours(1),
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initial_capital=10_000,
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execution=mbt.ExecutionConfig(allow_short=False, max_position_pct=1.0),
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fees=mbt.FeeConfig.zero(), # fee-free, for the example
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slippage=Slippage.fixed_bps(2),
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warmup_bars=60, # >= the longest window used
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)
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# -- Run ----------------------------------------------------------------------
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if __name__ == "__main__":
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root = os.path.join(os.path.dirname(__file__), "..")
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data_root = os.path.abspath(os.path.join(root, "data"))
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store = mbt.DataStore(
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data_root=data_root,
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metadata_db=os.path.abspath(os.path.join(root, "metadata", "metadata.sqlite")),
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arrow_dir=os.path.join(data_root, "mega"),
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)
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t0 = perf_counter()
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result = mbt.run(strategy, config, store)
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print(result.summary())
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print(f"\nElapsed: {perf_counter() - t0:.2f}s")
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